机器学习和化学测量方法用于使用ATR-FTIR指纹进行53根和根茎中文草药的高通量认证
Xiaoyu Liu1, Xiaokang Liu2, Jiawei Wang2
1School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 582400, China.
概括
这项研究引入了一种新的系统,使用减弱总反射度里埃变换红外光谱学 (ATR-FTIR) 和机器学习来准确识别根和根茎中国草本 (RRCH) 物种,克服它们相似外观所带来的挑战.
科学领域:
- 分析化学 分析化学
- 化学信息学 化学信息学
- 药理学是指药理学,即药理学是指药理学.
背景情况:
- 根和根茎中华草药 (RRCH) 的形态相似性带来了重大识别挑战.
- 准确的鉴定对于确保传统中医药的有效性和安全性至关重要.
研究的目的:
- 为植物学上相似的RRCH开发一个强大的歧视系统.
- 整合减弱总反射度里叶变换红外光谱 (ATR-FTIR) 与先进的机器学习技术进行增强的识别.
主要方法:
- 使用ATR-FTIR获得53种RRCH物种的光谱指纹.
- 应用多式机器学习框架,包括部分最小平方差异分析 (PLS-DA),t分布式静态邻居嵌入 (t-SNE) 和各种优化模型 (SVM,KNN,集体学习).
- 多变量分析和t-SNE预测用于分析化学特征和光谱-植物化学相关性.
主要成果:
- 多变量分析显示了53种RRCH物种中明显的化学分布模式.
- t-SNE预测显示了显著的集群分离,证实了光谱指纹-植物化学组成的相关性.
- 优化的支持矢量机 (SVM) 模型实现了100%的分类准确性,以更快的识别时间优于其他方法.
结论:
- 开发的ATR-FTIR和机器学习混合系统可以实现RRCH的高通量认证.
- 这种方法建立了一个可扩展的框架,用于草药质量标准化和药用材料的智能歧视.
- 该方法提供了通过振动频谱特征关系映射对化学标记物发现的洞察.
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